1. Field of the Invention
The invention relates to methods to forecast energy generation (source), consumption (sink) and storage so that energy balance is economically maintained with intelligent planning.
2. Description of the Related Art
Electricity is produced from non-renewable (NRE) and renewable energy sources (RE). A substantial portion of the electric power in the U.S. is mandated to be generated from renewable energy sources (RE). As shown in FIG. 1, three important sources of renewable energy are solar, wind and wave energy, which are all highly dependent on prevailing weather conditions. Forecasting the energy production and utilization levels with variable weather conditions is thus critical for effective planning, control, trading and transmission of power.
FIG. 1 is an algorithmic flow chart depicting present day stages of power applications and its relationship with renewable electric energy (RE), a first source of energy (e.g., solar, wind, wave and other) and with non-renewable electric energy (NRE), a second source of electric energy (fossil fuel and nuclear). The renewable electric energy sources each have their collective electrical energy output collected at a common source while concurrently, the non-renewable energy sources have their collective electrical energy output collected at a common source. The electrical energy stored at these sources is transmitted via a transmission grid (Power Transmission), depending upon the need of the system to a power storage means and/or to a first sink and a second sink (Power Distribution).
With respect to Power Consumption, the energy at the first sink derived from the renewable energy first source is environmentally dependent since it, and the renewable energy sources which generate the energy are totally dependent upon the weather which is uncertain. The energy at the second sink emanating from the non-renewable energy second source is directly controllable since it, and the non-renewable energy sources which generate the energy are able to supply energy upon demand.
FIG. 2A depicts a system for power transmission from a non-renewable energy source which is predictable. It shows an overall view of an NRE-based electric infrastructure. The information gathered by a sensor network can be composed into a uniform representation, denoted by electric state vector X. The collection of variables associated with an electrical system, such as voltage, current, power, energy (time integral of power), frequency, phase angle, etc., is referred to herein as its “electric state” at a given instant of time.
By way of illustration, the non-renewable energy source is a grid comprising power plant 10 that generates electricity which is directed to a transformer 11 that steps up the voltage in line 12 which line is connected to power transmission lines fixed on a high tension support 13 transmission. At a suitable location there is a line 14 to a neighborhood transformer 15 that steps down the voltage and directs the stepped down voltage to distribution lines 16 to carry electricity to houses. The voltage from distribution line 16 is transmitted to transformer 17 from which electricity enters house 18.
The state of the power system obtained by incorporating data readings from power sensors at locations A, B and C in the system and incorporating such data into Data Network 19. Power sensors A, B and C estimate the electric state by which one can map the state of power.
The state vector is a function of location (x,y) and is time (t) dependent. It can, for example, incorporate transmission line voltage (V), current (I), electrical power (P), electrical energy (E). Using moderate computing power, a real time visualization 20 of the power generated can be displayed to a customer.
Power Generation, Utilization and Storage
Given the production capacity of a plant, forecasting electric energy production from carbon or nuclear-based fuel is a trivial problem. As the percentage of contribution to the total energy mix from solar, wind and wave energy starts to grow from the present 3% to a targeted 30%, the forecasting of electric power production becomes a challenge, since solar, wind and wave-based power sources are highly dependent on weather conditions. Therefore, one of the primary focuses of the invention is to develop an algorithmic procedure to provide a robust forecasting method of renewable energy sources.
Forecasting energy utilization is also important. Drastic changes in weather patterns daily and even hourly, exacerbated by global warming, are expected to cause fluctuations in energy utilization. Winter heating and summer air-conditioning are two known energy-intensive processes that can cause power system instability, leading to blackouts and brownouts. Proper demand forecasts are required to avert these catastrophic conditions. For example, for the same outside temperature, heat loss from a building under a heavy wind can be substantially higher from that of a calm day. Thus, a capability to forecast and control the energy utilization (energy sink) becomes as critical as having an ability to forecast energy production. This is the second aspect of the invention.
Forecasting energy storage may well become more complex in the future. A hydro-electric plant provides an efficient form of energy storage, where the water is pumped back to a reservoir at a higher level using excess power generated during a period when demand is lower. The stored energy can be easily tracked by monitoring the volume of water pumped. New high energy density battery technologies and the emergence of a commuter plug-in hybrid electric vehicle (PHEV), energy storage is expected to be prevalent. For instance, the demand for charging the PHEV batteries is calculated to double energy demand in the evenings, thus requiring closer monitoring of the power distribution system.
New and yet to be invented technologies for storage of electric energy can further alter the dynamics and complicate energy generation and consumption patterns in the future. Hence, an ability to measure/estimate and track stored energy within a household or an industrial complex becomes an important component of predicting the energy state. While direct measurement of energy stored is the most convenient way to track the electric state, in some cases privacy considerations may become a barrier to measuring the stored energy within a household. Thus, innovative and less intrusive methods, coupled with an advanced metering infrastructure (AMI) will be required in the future. This is the third focus of the invention.
As the price of fuel fluctuates and weather patterns remain uncertain, the ability to forecast and maintain an economical balance between energy sources, sinks and storage elements becomes critical to an optimized energy infrastructure. Emergency petroleum reserves with unlimited capacity are not an affordable option. Excess power generation capacity can solve most of the challenges, but building and holding that capacity is also not economical. One can, however, envisage a sensible equilibrium condition at various system levels, if proactive planning and control is achieved with reliable forecast information.
Estimating “Future” Electrical State
Ongoing academic and industry research efforts attempt to improve the estimation accuracy of the electric variables of a grid by leveraging time-synchronized sensing of voltage and current phasors (or vectors). U.S. Pat. No. 7,499,816 to Scholtz discloses a method for the estimation of real-time power system quantities using time-synchronized measurements. Measurement technology, called a Phasor Measurement Unit (PMU), facilitates a new form of electric measurement suitable for characterizing wide electric grids. Two measurement technologies are widely used in the electric power industry. First one is called PMU technology which extracts magnitude and phase angle of voltage and current signals in a transmission line. This method which requires time synchronized measurements at different geographic location is made possible by the timing signals provided by GPS satellites (supporting the global positioning system (GPS)), and is expected to improve the confidence level of the estimates of an electric state. The second one is called SCADA (supervisory control and data acquisition) technology where voltage and current signals are obtained as a series of time-sampled data.
FIG. 3a depicts a summary of the disclosure of U.S. Pat. No. 7,499,816 wherein time-sampled SCADA data and GPS-synchronized Phasor PMU data are fed to a “Collect Sensor” based data base wherein the bits stored therein are converted in to physical variables X, such as volts, amps, etc.). Based upon said physical variables, a power system state is calculated according a formula. For example the formula: z=h(X)+e relates the measured data “z” to electric state X using a functional relationship h(X). Since the measurements are prone to error, the term “e” represents the uncertain error in the relationship between z and X. Using simple regression method, for a collection of “z” data with preassumed function “h” a least square estimate of X can be obtained.
FIGS. 3b-d describe prior art, where the electric state is estimated from information provided by a set of sensors. The sensors either produce time domain samples (FIG. 3b) or amplitude and phase information (FIG. 3c).
FIG. 3b) depicts sinusoidal curves representing the phase shift and amplitude difference in V and I curves as a function of time (time domain samples).
FIG. 3c) depicts a phasor showing the relationship among real power, reactive power and apparent power representing the quantities as vectors. Real power is the horizontal component of “VI” and reactive power is the vertical component of “VI”. Apparent power “VI” is directed along the hypotenuse V in the graph. Thus given the basic phasor measurement data, the electric power flowing in a transmission line can be readily computed.
FIG. 3d) depicts the relationship between measured and estimated voltage as a schematic example. The electric state is plotted using bus voltage as a function of time which is estimated from information provided by a set of sensors. Note that the “true” voltage is not easy to measure in the field due to sensor accuracy. However, using simple regression method and estimate of the voltage can be obtained as shown in FIG. 3d. Prior art focuses on estimating the present electric state using the sensors and formula that have been described.
Leveraging the sensor outputs, methods have been developed to estimate the present “state” of an electric grid with varying degrees of accuracy. Traditional state estimation techniques, such as least square estimator (LSE), have been applied in the electric industry since 1960 with varying degrees of success. To be economical, it is important to reduce the number of sensors while maximizing the geographic reach of estimation.
FIG. 4 corresponds to another prior art in which the state of an electric bus is “observed” without direct sensor-based measurement. Using a physics-based argument, for example Kirchoffs current law, the electric state of a bus can be computed from neighboring buses, assuming the impedances are known. Both forms of prior art contribute to estimation of the present state from a limited number of sensors.
Ultimately, the purpose of state estimation (for example, voltage and current levels of all electric buses within a designated zone) is to monitor, supervise or control an electric system in a designated zone. State estimation efforts are primarily concerned with the “present situation” and not about the future condition of the grid.
The invention presented in this disclosure assumes that improved present state estimation using SCADA, PMU and AMI will be available for efforts to forecast future operations. The invention addresses the challenge of power and energy forecasts from the production and utilization viewpoints specifically under variable weather conditions.
Reliable forecasting of electric energy production and utilization can lead to enhanced management, planning, control, trading and transmission of electricity in the future. Forecasting requires robust and computer-implementable methods.
The invention describes a method to forecast the power and energy state of an electric system. In general, the state of a system can encompass several variables. The collection of variables associated with an electrical system, such as voltage, current, power, energy (time integral of power), frequency, phase angle, etc., is referred to herein as its “electric state” at a given instant of time. This system can be an individual home, a cluster of homes, a municipality, an industrial plant, or any kind of power generation facility, among many others. The term specifically refers to entities with different physical scales. The methods presented can be integrated into a service operation, where any interested business can subscribe to receive or interact with the service provider to inquire about their past, present and future “electric state” to make effective decisions.
There is thus a need to augment the state estimation technology under development to achieve the following capabilities:
1). Forecast energy production (source) specifically from the renewable energy sector,
2). Forecast energy consumption (sink) made complicated by rapid weather changes,
3). Estimate and track energy stored in households, industrial complexes, etc.
4) Control the optimum energy utilization method to meet constraints desired by a household, an industrial complex or other entity,
5). Integrate capabilities 1.) through 4.) to provide a 24/7 forecasting service.